examples/landsat8-l1.ipynb

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    "Intake-STAC + Landsat8\n",
    "================\n",
    "\n",
    "In this notebook, we'll take a look at some of the functionality in Intake-STAC by exploring an example STAC 1.0 catalog with a Landsat-8 L1 public data on AWS (https://registry.opendata.aws/landsat-8/)"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import intake\n",
    "print(intake.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for development\n",
    "#%load_ext autoreload\n",
    "#%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# intake checks for registered drivers when imported\n",
    "# You should see 'stac_catalog, stac_collection, stac_item, and stac_item_collection' if intake-stac is installed\n",
    "list(intake.registry)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load root catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open a catalog and list subcatalogs / collections\n",
    "url = 'https://raw.githubusercontent.com/sat-utils/sat-stac/master/test/catalog/catalog.json'\n",
    "cat = intake.open_stac_catalog(url)\n",
    "cat.metadata.keys()\n",
    "list(cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drill down into subcatalogs\n",
    "subcat = cat['stac-catalog-eo']\n",
    "list(subcat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drill further down into subcatalogs\n",
    "subcat1 = subcat['landsat-8-l1']\n",
    "list(subcat1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "subcat1.metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NOTE: would be good to have easy way for user to distinguish between catalogs and items\n",
    "print(type(subcat1._stac_obj))\n",
    "item = subcat1['LC08_L1TP_152038_20200611_20200611_01_RT']\n",
    "print(type(item._stac_obj))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "item.metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check assets\n",
    "list(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display thumbnail\n",
    "from IPython.display import Image\n",
    "Image(item['thumbnail'].urlpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load single band into xarray data array\n",
    "da = item.B4.to_dask()\n",
    "da"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stacking bands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bands = ['nir','red']\n",
    "stack = item.stack_bands(bands)\n",
    "type(stack)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bug? currently need to specify chunks:\n",
    "da = stack(chunks=dict(band=1, x=2048, y=2048)).to_dask()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that common names were mapped to standard names 'nir'->'B5', 'red'->'B4'\n",
    "# An alternative organization is to store as a DataSet with common names:\n",
    "da['band'] = bands\n",
    "ds = da.to_dataset(dim='band')\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we can calculate band indices of subregions easily\n",
    "NDVI = (ds['nir'] - ds['red']) / (ds['nir'] + ds['red'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "NDVI.isel(y=slice(2000,3000), x=slice(1500,2000)).plot.imshow(cmap='BrBG', vmin=-1, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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